{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58a76470",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright 2021 Google LLC\n",
    "\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7caba06b",
   "metadata": {},
   "source": [
    "# BigQuery Audit Log Anomaly Detection\n",
    "This example uses BigQuery Data Access Audit logs (cloudaudit_googleapis_com_data_access_*) to identify outlier and anomalous usage within your BigQuery data environment - it uses estimated cost (calculated by $5USD/TB) and total Tables Processed per job as the metric to identify anomalies. \n",
    "\n",
    "This example show cases two methods for identifying anomalies: \n",
    "1. <b> Outliers in groups: </b> This method looks for a datapoint that differs signicantly from others. This means that it identifies users who use BQ way more or less than others. \n",
    "2.  <b> Time Series Analysis: </b> Looking for outliers in periodic trends by looking at audit logs chronologically. This method has an underlying assumption that BigQuery usage has trends.\n",
    "\n",
    "Other possible metrics include: \n",
    "* 'runtimeMs',\n",
    "* 'runtimeSecs',\n",
    "* 'lagtimeMs',\n",
    "* 'lagtimeSecs',\n",
    "* 'totalLoadOutputBytes',\n",
    "* 'totalSlotMs',\n",
    "* 'avgSlotsMS',\n",
    "* 'totalTablesProcessed',\n",
    "* 'totalViewsProcessed',\n",
    "* 'totalProcessedBytes',\n",
    "* 'totalBilledBytes',\n",
    "* 'querylength', \n",
    "* 'estimatedCostUsd', "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c8f2a44",
   "metadata": {},
   "source": [
    "## Setup\n",
    "This cell downloads all requirements and creates required views for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48a5037d",
   "metadata": {},
   "outputs": [],
   "source": [
    "! echo \"Installing Dependencies...\"\n",
    "! pip install -r requirements.txt || echo 'Error installing other dependencies'\n",
    "! jupyter nbextension enable --py widgetsnbextension\n",
    "! jupyter serverextension enable voila --sys-prefix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3cdc7d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from google.cloud import bigquery\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "import viewsFactory as Vf\n",
    "from google.cloud import bigquery\n",
    "from google.cloud.exceptions import NotFound\n",
    "from IPython.display import display, Markdown, Latex\n",
    "import ipywidgets as widgets\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import cufflinks as cf\n",
    "import plotly.io as pio\n",
    "cf.go_offline()\n",
    "init_notebook_mode(connected='true')\n",
    "from ipywidgets import HBox, VBox, IntSlider, interactive_output, FloatSlider, interact, interact_manual\n",
    "from traitlets import directional_link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "66466d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "jupyter = 'plotly_mimetype+notebook_connected'\n",
    "github = 'svg'\n",
    "\n",
    "# Comment out the Github line and Uncomment the jupyter line when running in jupyter to see interactive graphs\n",
    "pio.renderers.default = github\n",
    "# pio.renderers.default = jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e0de240",
   "metadata": {},
   "source": [
    "### Importing environment variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f3a99565",
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "load_dotenv(\"var.env\")\n",
    "\n",
    "project_id       = os.environ.get(\"project_id\")\n",
    "data_project_id  = os.environ.get(\"data_project_id\")\n",
    "dataset_id       = os.environ.get(\"dataset_id\")\n",
    "audit_log_table  = os.environ.get(\"audit_log_table\")\n",
    "location         = os.environ.get(\"location\")\n",
    "audit_log_partitioned = (os.environ.get(\"audit_log_partitioned\") == \"True\")\n",
    "\n",
    "## Destination View \n",
    "destination_project_id   = os.environ.get(\"destination_project_id\")\n",
    "destination_dataset_id   = os.environ.get(\"destination_dataset_id\")\n",
    "summary_table_name       = os.environ.get(\"summary_table_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "67d23ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_to_df(query):\n",
    "    \"\"\"Run query string in BigQuery and return pandas dataframe from query result\"\"\"\n",
    "    query_job = client.query(\n",
    "        query,\n",
    "        # Location must match that of the dataset(s) referenced in the query.\n",
    "        location=location,\n",
    "    )  # API request - starts the query\n",
    "\n",
    "    df = query_job.to_dataframe()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cef14c2",
   "metadata": {},
   "source": [
    "### Creating summary view of Audit Log table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ba1fd8b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# %%capture\n",
    "client = bigquery.Client(location=location, project=project_id)\n",
    "print(\"Client creating using default project: {}\".format(client.project))\n",
    "\n",
    "view = Vf.CreateView(project_id, data_project_id, dataset_id, audit_log_table, location, audit_log_partitioned, \n",
    "                     destination_project_id, destination_dataset_id, summary_table_name) \n",
    "\n",
    "view.create_all_job_view()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b91026",
   "metadata": {},
   "source": [
    "## Understanding the data - distributions and statistics\n",
    "This section shows an example of a function to help in understanding the data for different metrics. Change the metric variable `metric_understanding` if you want to explore a different metric. \n",
    "\n",
    "<b>Note:</b> Since the values are Log Values, metrics which has zero values the below query will error with a ln(0) error, remove the `LOG` in the query if that happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "727957ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Distribution and Statistic plots Controls\n",
    "metric_understanding = 'estimatedCostUsd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03e8c767",
   "metadata": {},
   "outputs": [
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font-size: 17px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Log scale distribution of estimatedCostUsd per job</text></g><g class=\"g-xtitle\"><text class=\"xtitle\" x=\"350\" y=\"460.3\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">estimatedCostUsd</text></g><g class=\"g-ytitle\"><text class=\"ytitle\" transform=\"rotate(-90,31.840625000000003,260)\" x=\"31.840625000000003\" y=\"260\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Count</text></g></g></svg>"
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       "<p> <b> Descriptive Statistics for estimatedCostUsd:</b></p>"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "buck_query = \"\"\"\n",
    "            SELECT\n",
    "              MAX(LOG({metric})) AS maxvalue,\n",
    "              MIN(LOG({metric})) AS minvalue\n",
    "            FROM\n",
    "              `{project_id}.{destination_dataset_id}.{summary_table_name}`\n",
    "            WHERE\n",
    "              {metric} IS NOT NULL\n",
    "              AND {metric} <> 0\n",
    "            \"\"\".format(project_id = destination_project_id, \n",
    "                       destination_dataset_id = destination_dataset_id , \n",
    "                       summary_table_name = summary_table_name,\n",
    "                       metric = metric_understanding)\n",
    "\n",
    "buk_df = query_to_df(buck_query)\n",
    "buckets = []\n",
    "val = buk_df.minvalue[0]\n",
    "for r in range(300+1): \n",
    "    buckets.append(val)\n",
    "    val += (abs(buk_df.maxvalue[0] - buk_df.minvalue[0]))/300\n",
    "\n",
    "\n",
    "dist_metric_query = \"\"\"\n",
    "                   WITH\n",
    "                      help AS (\n",
    "                      SELECT\n",
    "                        bucket,\n",
    "                        CONCAT(IFNULL(ranges[SAFE_OFFSET(bucket - 1)],\n",
    "                            0), '-', ranges[SAFE_OFFSET(bucket)]) AS prange,\n",
    "                        COUNT(*) AS countmetric,\n",
    "                        MAX(IFNULL(ranges[SAFE_OFFSET(bucket - 1)],\n",
    "                            0)) AS startRange,\n",
    "                        MAX(ranges[SAFE_OFFSET(bucket)]) AS endRange\n",
    "                      FROM\n",
    "                        `{project_id}.{destination_dataset_id}.{summary_table_name}`,\n",
    "                        UNNEST([STRUCT({buckets} AS ranges)]),\n",
    "                        UNNEST([RANGE_BUCKET(LOG({metric}),\n",
    "                            ranges)]) bucket\n",
    "                      WHERE\n",
    "                        {metric} IS NOT NULL\n",
    "                        AND {metric} <> 0\n",
    "                      GROUP BY\n",
    "                        1,\n",
    "                        2\n",
    "                      ORDER BY\n",
    "                        bucket )\n",
    "                    SELECT\n",
    "                      *\n",
    "                    FROM\n",
    "                      help\n",
    "                    WHERE\n",
    "                      prange IS NOT NULL\n",
    "                \"\"\".format(project_id = destination_project_id, \n",
    "                           destination_dataset_id = destination_dataset_id , \n",
    "                           summary_table_name = summary_table_name,\n",
    "                           metric = metric_understanding,\n",
    "                           buckets = buckets\n",
    "                           )\n",
    "    \n",
    "dist_metric_df = query_to_df(dist_metric_query)\n",
    "\n",
    "fig = go.Figure(data=[go.Bar(\n",
    "x=dist_metric_df['startRange'] + 0.5*(dist_metric_df['endRange'] - dist_metric_df['startRange']),\n",
    "y=dist_metric_df['countmetric'],\n",
    "width=dist_metric_df['endRange'] - dist_metric_df['startRange'], \n",
    "customdata = dist_metric_df.to_numpy(),\n",
    "hovertemplate='<b>Range: %{customdata[1]}</b><br>Count: %{customdata[2]:.3f}<br>',\n",
    ")])\n",
    "\n",
    "\n",
    "fig.update_layout(title_text='Log scale distribution of {} per job'.format(metric_understanding), \n",
    "                  xaxis_title_text=metric_understanding, # xaxis label\n",
    "                  yaxis_title_text='Count',)\n",
    "\n",
    "fig.show()\n",
    "\n",
    "stat_query = \"\"\"\n",
    "                WITH\n",
    "                  mode AS (\n",
    "                  SELECT\n",
    "                    {metric} AS modevalue,\n",
    "                  FROM\n",
    "                    `{proj}.{ds}.{summ}`\n",
    "                  WHERE\n",
    "                    {metric} <> 0\n",
    "                    AND {metric} IS NOT NULL\n",
    "                  GROUP BY\n",
    "                    {metric}\n",
    "                  ORDER BY\n",
    "                    COUNT(*) DESC\n",
    "                  LIMIT\n",
    "                    1 )\n",
    "                SELECT\n",
    "                  COUNT(NULLIF(main.{metric},\n",
    "                      0)) AS totalcount,\n",
    "                  AVG({metric}) AS average,\n",
    "                  MAX({metric}) AS maxvalue,\n",
    "                  MIN({metric}) AS minvalue,\n",
    "                  MAX({metric}) - MIN({metric}) AS rangevalues,\n",
    "                  MAX(modevalue) AS modevalue,\n",
    "                  MAX(medianvalue) AS medianvalue,\n",
    "                FROM\n",
    "                  `{proj}.{ds}.{summ}` main,\n",
    "                  mode,\n",
    "                  (\n",
    "                  SELECT\n",
    "                    PERCENTILE_CONT({metric},\n",
    "                      0.5) OVER() AS medianvalue\n",
    "                  FROM\n",
    "                    `{proj}.{ds}.{summ}`\n",
    "                  LIMIT\n",
    "                    1 )\n",
    "                WHERE\n",
    "                  {metric} <> 0\n",
    "                  AND {metric} IS NOT NULL\n",
    "            \"\"\".format(metric=metric_understanding, \n",
    "                       proj=destination_project_id, \n",
    "                       ds=destination_dataset_id, \n",
    "                       summ=summary_table_name)\n",
    "\n",
    "stats_df = query_to_df(stat_query)\n",
    "stats_df = stats_df.T.round(5)\n",
    "\n",
    "fig = go.Figure(data=[go.Table(header=dict(values=['Statistic', 'Value']),\n",
    "             cells=dict(values=[stats_df.index, stats_df.iloc[:, 0]]))\n",
    "                 ])\n",
    "display(Markdown('<p> <b> Descriptive Statistics for {}:</b></p>'.format(metric_understanding)))\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e4e7c7",
   "metadata": {},
   "source": [
    "1. The distribution plot is useful to understand the variability of your BigQuery usage related to the metric under investigation. Metrics with longer tails in the distribution plot or with spike in high or low values could be indication for need for further analysis.\n",
    "\n",
    "2. The descriptive statistics overview is useful for quick understanding of the environments. For example, have a abnormally high maximium value could indicate a good candidate for further evaluation. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a783b637",
   "metadata": {},
   "source": [
    "***\n",
    "## 1. Outliers in groups\n",
    "Generate insights of usage patterns around different groups (which can be one of the following: 'principalEmail', 'eventName', 'projectId', 'dayOfWeek', 'hourOfDay'). This provides information on anomalous usage and pinpoints areas for strategic optimization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c61459c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outlier Group Analysis controls\n",
    "metric_outlier = 'estimatedCostUsd' # The query metric to investigate.\n",
    "group          =  \"principalEmail\" # Group to identify outliers in\n",
    "sigma          = 1.5 # Value of SD from mean to be considered an outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "854af083",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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     "metadata": {},
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    {
     "data": {
      "text/markdown": [
       "<h2> Summary </h2>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<p> Hover over the datapoints in the plot to see the group labels. </p>"
      ],
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       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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    },
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     "data": {
      "text/markdown": [
       "<h4> The biggest outlier found is: <b>josie@example.com </b> with an average of       0.41 which is     2.0157 SD from the mean. </h4>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric_query = \"\"\" WITH\n",
    "                      get_metrics AS (\n",
    "                      SELECT\n",
    "                        {group} AS groupedby,\n",
    "                        AVG({metric}) AS aggaverage,\n",
    "                        AVG(AVG({metric})) OVER () AS mean,\n",
    "                        STDDEV(AVG({metric})) OVER () AS sd\n",
    "                      FROM\n",
    "                        `{project_id}.{destination_dataset_id}.{summary_table_name}`\n",
    "                      GROUP BY\n",
    "                        groupedby)\n",
    "                    SELECT\n",
    "                      groupedby,\n",
    "                      aggaverage,\n",
    "                      (aggaverage - mean)/sd AS zscore,\n",
    "                    IF\n",
    "                      (ABS((aggaverage - mean)/sd) > {out},\n",
    "                        1,\n",
    "                        0) AS outlier\n",
    "                    FROM\n",
    "                      get_metrics\n",
    "                    ORDER BY\n",
    "                      zscore DESC\n",
    "                \"\"\".format(metric = metric_outlier, \n",
    "                           group = group, \n",
    "                           project_id = destination_project_id, \n",
    "                           destination_dataset_id = destination_dataset_id , \n",
    "                           summary_table_name = summary_table_name, \n",
    "                           out = sigma)\n",
    "metric_df    = query_to_df(metric_query)\n",
    "\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Box(\n",
    "                x        = metric_df[\"aggaverage\"],\n",
    "                pointpos = -2,\n",
    "                opacity  = 1,\n",
    "                customdata = metric_df.to_numpy(),\n",
    "                name     = group,\n",
    "                hovertemplate='<b>%{customdata[0]}</b><br><br>Aggregated Average: %{customdata[1]:.3f}<br>SD from Mean: %{customdata[2]:.3f}',\n",
    "                boxpoints = \"all\",\n",
    "                selectedpoints = metric_df[metric_df['outlier']==1].index,\n",
    "                selected = dict(marker = dict( color = \"#DB4437\", opacity = 1)), \n",
    "                text=metric_df[\"aggaverage\"], \n",
    "                ))\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Average {metric} grouped by {group} with red points as outliers > {sd} sd from mean\".format(metric=metric_outlier, group=group, sd=sigma), \n",
    "    xaxis_title= metric_outlier)\n",
    "\n",
    "biggest = metric_df[metric_df['outlier']==1]    \n",
    "fig.show()\n",
    "\n",
    "display(Markdown('<h2> Summary </h2>'))\n",
    "display(Markdown('<p> Hover over the datapoints in the plot to see the group labels. </p>'))\n",
    "\n",
    "\n",
    "if biggest.empty: \n",
    "    display(Markdown('<h4> There are no outliers greater than your selection of {sigma} SD from the mean. </h4>'.format(sigma=sigma)))\n",
    "else:\n",
    "    display(Markdown('<h4> The biggest outlier found is: <b>{} </b> with an average of {:10.2f} which is {:10.4f} SD from the mean. </h4>'.format(metric_df[metric_df['outlier']==1].iloc[0].groupedby, \n",
    "                                                                                                                                     metric_df[metric_df['outlier']==1].iloc[0].aggaverage, \n",
    "                                                                                                                                     metric_df[metric_df['outlier']==1].iloc[0].zscore)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c52a7ed",
   "metadata": {},
   "source": [
    "The outlier highlighted in red represents an entity within the group that could require further optimization. The boxplot allows quick identification that an individual has much a higher average per query compared to other members in the group.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03117ead",
   "metadata": {},
   "source": [
    "***\n",
    "# 2. Time Series Analysis\n",
    "\n",
    "Uses 'totalTablesProcessed' as the metric for analysis. The analysis is carried out as follows: \n",
    "1. Calculated the average totalTablesProcessed per query per hour.\n",
    "2. Carry out STL composition to decompose the time series into Seasonlity and Trend which is used to calculate an estimate: <br>\n",
    "`estimation = trend + seasonal`\n",
    "3. Outliers are determined by finding the difference between the estimated reconstructed time series and the actual value (residual). Any residual above a sigma threshold (user defined) is flagged as an outlier in the plot below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e545c46",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.seasonal import STL\n",
    "from plotly.subplots import make_subplots\n",
    "from scipy import fftpack\n",
    "from traitlets import directional_link\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1d16c3e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time Series Controls\n",
    "metric_time = 'totalTablesProcessed' # The query metric to investigate.\n",
    "sigma_time  = 3 # Value of SD from mean to be considered an outlier.\n",
    "time        = 'HOUR' # The time interval to aggregate on. For example, selecting HOUR will generate an hourly average of the selected metric.\n",
    "period      = 24 # Taking each day as one cyclic period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "87f8e881",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Query for STL decomposition\n",
    "stl_query = \"\"\"\n",
    "        SELECT\n",
    "          AVG({metric}) AS avgmetric,\n",
    "          TIMESTAMP_TRUNC(createTime, {time}) AS time\n",
    "        FROM\n",
    "          `{project_id}.{destination_dataset_id}.{summary_table_name}`\n",
    "        GROUP BY\n",
    "          2\n",
    "        ORDER BY\n",
    "          2\n",
    "        \"\"\".format(project_id = destination_project_id, \n",
    "                   destination_dataset_id = destination_dataset_id , \n",
    "                   summary_table_name = summary_table_name,\n",
    "                   metric = metric_time, \n",
    "                   time = time)    \n",
    "\n",
    "\n",
    "stl_o_df    = query_to_df(stl_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "15e3217b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#STL DECOMPOSITION\n",
    "stl_df = stl_o_df.reset_index()\n",
    "stl_df = stl_df.set_index('time').drop(columns = ['index'])\n",
    "stl = STL(stl_df, period=period)\n",
    "result = stl.fit()\n",
    "seasonal, trend, resid = result.seasonal, result.trend, result.resid\n",
    "estimated = trend + seasonal\n",
    "resid_mu = resid.mean()\n",
    "resid_dev = resid.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "40ab9c1f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "<h2> Summary </h2>"
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      "text/markdown": [
       "<p> <b> There are potential outliers in the following HOUR(s): </b></p>"
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   "source": [
    "# Plots for STL \n",
    "fig_2 = make_subplots(rows=4, cols=1, row_heights=[15 for x in range(4)],\n",
    "                specs=[[{'type':'xy'}], [{'type':'xy'}], [{'type':'xy'}], [{'type':'xy'}]], \n",
    "                horizontal_spacing = 0.2, \n",
    "                subplot_titles=['Original Series', 'Trend','Seasonal','Residual'])\n",
    "\n",
    "\n",
    "fig_2.add_trace(go.Scatter(x=stl_df.index, y=stl_df['avgmetric'], name='original'), 1,1)\n",
    "fig_2.add_trace(go.Scatter(x=trend.index, y=trend, name='Trend'), 2,1)\n",
    "fig_2.add_trace(go.Scatter(x=seasonal.index, y=seasonal, name='Seasonal'), 3,1)\n",
    "fig_2.add_trace(go.Scatter(x=resid.index, y=resid, name='Resid'), 4,1)\n",
    "\n",
    "fig_2.update_layout(\n",
    "    autosize=True,\n",
    "    width=1000,\n",
    "    height=800,\n",
    "    title='STL Decomposition')\n",
    "\n",
    "fig_2.show()\n",
    "\n",
    "upper = resid_mu + sigma_time*resid_dev\n",
    "anomalies = stl_df[(resid > upper)]\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=stl_df.index, y=stl_df['avgmetric'], name='original'))\n",
    "fig.add_trace(go.Scatter(x=estimated.index, y=estimated, name='estimation'))\n",
    "fig.add_trace(go.Scatter(x=anomalies.index, y=anomalies.avgmetric,\n",
    "                        mode='markers+text',\n",
    "                        marker=dict(color='red'),\n",
    "                        marker_symbol='x',\n",
    "                        marker_line_width=2, \n",
    "                        marker_size=10,\n",
    "                        name='outliers', \n",
    "                        textposition=\"top center\",\n",
    "                        text=np.round(anomalies.avgmetric.values, 2)))\n",
    "\n",
    "\n",
    "display(Markdown('<p> <b> Outliers are detected if their residual (estimation - actual) is more than {sigma} from the residual mean </b></p>'.format(sigma=sigma_time)))\n",
    "fig.update_layout(\n",
    "            title=\"Detected outliers with residual > {sigma} SD from mean\".format(sigma=sigma_time), \n",
    "            xaxis_title=\"Creation Time\", \n",
    "            yaxis_title=metric_time\n",
    "                    )    \n",
    "\n",
    "fig.show()\n",
    "anomalies = anomalies.round(2)\n",
    "fig = go.Figure(data=[go.Table(header=dict(values=['Time', 'Average {}'.format(metric_time)]),\n",
    "             cells=dict(values=[anomalies.index, anomalies.avgmetric] ))\n",
    "                 ])\n",
    "display(Markdown('<h2> Summary </h2>'))\n",
    "\n",
    "if anomalies.empty:\n",
    "    display(Markdown('<h4> There are no outliers greater than your selection of {sigma} SD from the mean. </h4>'.format(sigma=sigma_time)))\n",
    "\n",
    "else:\n",
    "    display(Markdown('<p> <b> There are potential outliers in the following {}(s): </b></p>'.format(time)))\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180f192c",
   "metadata": {},
   "source": [
    "The outliers detected to have values much higher than the prediction indicate a period of time that could benefit from further analysis - allowing Data Engineers to more quickly zoom into potential problem times. In a business use case, individuals who understand the business may expect these spikes to be normal - for example when more analysis is being done during a sale period in an E-commerce business. "
   ]
  }
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